Data Science Machine Learning by Levene Mark (31 results)

Paperback. Condition: Very Good. 1. It's a well-cared-for item that has seen limited use. The item may show minor signs of wear. All the text is legible, with all pages included. It may have slight markings and/or highlighting.

paperback. Condition: New. Brand new book, sourced directly from publisher. Dispatch time is 3-4 days from our warehouse. Book will be sent in robust, secure packaging to ensure it reaches you securely.

Condition: As New. Unread book in perfect condition.

Condition: New.

Paperback. Condition: New. An accessible introduction to applied data science and machine learning, with minimal math and code required to master the foundational and technical aspects of data science. In Just Enough Data Science and Machine Learning, authors Mark Levene and Martyn Harris present a comprehensive and accessible introduction to data science. It allows the readers to develop an intuition behind the methods adopted in both data science and machine learning, which is the algorithmic component of data science involving the discovery of patterns from input data. This book looks at data science from an applied perspective, where emphasis is placed on the algorithmic aspects of data science and on the fundamental statistical concepts necessary to understand the subject. The book begins by exploring the nature of data science and its origins in basic statistics. The authors then guide readers through the essential steps of data science, starting with exploratory data analysis using visualisation tools. They explain the process of forming hypotheses, building statistical models, and utilising algorithmic methods to discover patterns in the data. Finally, the authors discuss general issues and preliminary concepts that are needed to understand machine learning, which is central to the discipline of data science. The book is packed with practical examples and real-world data sets throughout to reinforce the concepts. All examples are supported by Python code external to the reading material to keep the book timeless. Notable features of this book: Clear explanations of fundamental statistical notions and conceptsCoverage of various types of data and techniques for analysisIn-depth exploration of popular machine learning tools and methodsInsight into specific data science topics, such as social networks and sentiment analysisPractical examples and case studies for real-world applicationRecommended further reading for deeper exploration of specific topics.…

PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.

Paperback or Softback. Condition: New. Just Enough Data Science and Machine Learning: Essential Tools and Techniques. Book.

Condition: New.

Condition: Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.

Paperback. Condition: new. Paperback. An accessible introduction to applied data science and machine learning, with minimal math and code required to master the foundational and technical aspects of data science. In Just Enough Data Science and Machine Learning, authors Mark Levene and Martyn Harris present a comprehensive and accessible introduction to data science. It allows the readers to develop an intuition behind the methods adopted in both data science and machine learning, which is the algorithmic component of data science involving the discovery of patterns from input data. This book looks at data science from an applied perspective, where emphasis is placed on the algorithmic aspects of data science and on the fundamental statistical concepts necessary to understand the subject. The book begins by exploring the nature of data science and its origins in basic statistics. The authors then guide readers through the essential steps of data science, starting with exploratory data analysis using visualisation tools. They explain the process of forming hypotheses, building statistical models, and utilising algorithmic methods to discover patterns in the data. Finally, the authors discuss general issues and preliminary concepts that are needed to understand machine learning, which is central to the discipline of data science. The book is packed with practical examples and real-world data sets throughout to reinforce the concepts. All examples are supported by Python code external to the reading material to keep the book timeless. Notable features of this book: Clear explanations of fundamental statistical notions and conceptsCoverage of various types of data and techniques for analysisIn-depth exploration of popular machine learning tools and methodsInsight into specific data science topics, such as social networks and sentiment analysisPractical examples and case studies for real-world applicationRecommended further reading for deeper exploration of specific topics. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condition: As New. Unread book in perfect condition.

paperback. Condition: New.

Paperback / softback. Condition: New. New copy - Usually dispatched within 2 working days.

Condition: New. 1st edition NO-PA16APR2015-KAP.

Paperback. Condition: New. An accessible introduction to applied data science and machine learning, with minimal math and code required to master the foundational and technical aspects of data science. In Just Enough Data Science and Machine Learning, authors Mark Levene and Martyn Harris present a comprehensive and accessible introduction to data science. It allows the readers to develop an intuition behind the methods adopted in both data science and machine learning, which is the algorithmic component of data science involving the discovery of patterns from input data. This book looks at data science from an applied perspective, where emphasis is placed on the algorithmic aspects of data science and on the fundamental statistical concepts necessary to understand the subject. The book begins by exploring the nature of data science and its origins in basic statistics. The authors then guide readers through the essential steps of data science, starting with exploratory data analysis using visualisation tools. They explain the process of forming hypotheses, building statistical models, and utilising algorithmic methods to discover patterns in the data. Finally, the authors discuss general issues and preliminary concepts that are needed to understand machine learning, which is central to the discipline of data science. The book is packed with practical examples and real-world data sets throughout to reinforce the concepts. All examples are supported by Python code external to the reading material to keep the book timeless. Notable features of this book: Clear explanations of fundamental statistical notions and conceptsCoverage of various types of data and techniques for analysisIn-depth exploration of popular machine learning tools and methodsInsight into specific data science topics, such as social networks and sentiment analysisPractical examples and case studies for real-world applicationRecommended further reading for deeper exploration of specific topics.…

Condition: New.

Condition: New. In.

Paperback. Condition: New.

Condition: New.

Condition: New. 2025. 1st Edition. paperback. . . . . .

paperback. Condition: New. Special order direct from the distributor.

Taschenbuch. Condition: Neu. Neuware -An accessible introduction to applied data science and machine learning, with minimal math and code required to master the foundational and technical aspects of data science. In Just Enough Data Science and Machine Learning, authors Mark Levene and Martyn Harris present a comprehensive and accessible introduction to data science. It allows the readers to develop an intuition behind the methods adopted in both data science and machine learning, which is the algorithmic component of data science involving the discovery of patterns from input data. This book looks at data science from an applied perspective, where emphasis is placed on the algorithmic aspects of data science and on the fundamental statistical concepts necessary to understand the subject. The book begins by exploring the nature of data science and its origins in basic statistics. The authors then guide readers through the essential steps of data science, starting with exploratory data analysis using visualisation tools. They explain the process of forming hypotheses, building statistical models, and utilising algorithmic methods to discover patterns in the data. Finally, the authors discuss general issues and preliminary concepts that are needed to understand machine learning, which is central to the discipline of data science. The book is packed with practical examples and real-world data sets throughout to reinforce the concepts. All examples are supported by Python code external to the reading material to keep the book timeless. Notable features of this book: - Clear explanations of fundamental statistical notions and concepts - Coverage of various types of data and techniques for analysis - In-depth exploration of popular machine learning tools and methods - Insight into specific data science topics, such as social networks and sentiment analysis - Practical examples and case studies for real-world application - Recommended further reading for deeper exploration of specific topics.…

Paperback. Condition: Brand New. 224 pages. 7.00x0.45x9.13 inches. In Stock.

Condition: New. 2025. 1st Edition. paperback. . . . . . Books ship from the US and Ireland.

Paperback. Condition: New. An accessible introduction to applied data science and machine learning, with minimal math and code required to master the foundational and technical aspects of data science. In Just Enough Data Science and Machine Learning, authors Mark Levene and Martyn Harris present a comprehensive and accessible introduction to data science. It allows the readers to develop an intuition behind the methods adopted in both data science and machine learning, which is the algorithmic component of data science involving the discovery of patterns from input data. This book looks at data science from an applied perspective, where emphasis is placed on the algorithmic aspects of data science and on the fundamental statistical concepts necessary to understand the subject. The book begins by exploring the nature of data science and its origins in basic statistics. The authors then guide readers through the essential steps of data science, starting with exploratory data analysis using visualisation tools. They explain the process of forming hypotheses, building statistical models, and utilising algorithmic methods to discover patterns in the data. Finally, the authors discuss general issues and preliminary concepts that are needed to understand machine learning, which is central to the discipline of data science. The book is packed with practical examples and real-world data sets throughout to reinforce the concepts. All examples are supported by Python code external to the reading material to keep the book timeless. Notable features of this book: Clear explanations of fundamental statistical notions and conceptsCoverage of various types of data and techniques for analysisIn-depth exploration of popular machine learning tools and methodsInsight into specific data science topics, such as social networks and sentiment analysisPractical examples and case studies for real-world applicationRecommended further reading for deeper exploration of specific topics.…

Taschenbuch. Condition: Neu. Just Enough Data Science and Machine Learning | Essential Tools and Techniques | Mark Levene (u. a.) | Taschenbuch | Englisch | 2024 | Pearson Education | EAN 9780138340742 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu. …

Taschenbuch. Condition: Neu. Neuware - An accessible introduction to applied data science and machine learning, with minimal math and code required to master the foundational and technical aspects of data science. In Just Enough Data Science and Machine Learning, authors Mark Levene and Martyn Harris present a comprehensive and accessible introduction to data science. It allows the readers to develop an intuition behind the methods adopted in both data science and machine learning, which is the algorithmic component of data science involving the discovery of patterns from input data. This book looks at data science from an applied perspective, where emphasis is placed on the algorithmic aspects of data science and on the fundamental statistical concepts necessary to understand the subject. The book begins by exploring the nature of data science and its origins in basic statistics. The authors then guide readers through the essential steps of data science, starting with exploratory data analysis using visualisation tools. They explain the process of forming hypotheses, building statistical models, and utilising algorithmic methods to discover patterns in the data. Finally, the authors discuss general issues and preliminary concepts that are needed to understand machine learning, which is central to the discipline of data science. The book is packed with practical examples and real-world data sets throughout to reinforce the concepts. All examples are supported by Python code external to the reading material to keep the book timeless. Notable features of this book: - Clear explanations of fundamental statistical notions and concepts - Coverage of various types of data and techniques for analysis - In-depth exploration of popular machine learning tools and methods - Insight into specific data science topics, such as social networks and sentiment analysis - Practical examples and case studies for real-world application - Recommended further reading for deeper exploration of specific topics.…

Paperback. Condition: new. Paperback. An accessible introduction to applied data science and machine learning, with minimal math and code required to master the foundational and technical aspects of data science. In Just Enough Data Science and Machine Learning, authors Mark Levene and Martyn Harris present a comprehensive and accessible introduction to data science. It allows the readers to develop an intuition behind the methods adopted in both data science and machine learning, which is the algorithmic component of data science involving the discovery of patterns from input data. This book looks at data science from an applied perspective, where emphasis is placed on the algorithmic aspects of data science and on the fundamental statistical concepts necessary to understand the subject. The book begins by exploring the nature of data science and its origins in basic statistics. The authors then guide readers through the essential steps of data science, starting with exploratory data analysis using visualisation tools. They explain the process of forming hypotheses, building statistical models, and utilising algorithmic methods to discover patterns in the data. Finally, the authors discuss general issues and preliminary concepts that are needed to understand machine learning, which is central to the discipline of data science. The book is packed with practical examples and real-world data sets throughout to reinforce the concepts. All examples are supported by Python code external to the reading material to keep the book timeless. Notable features of this book: Clear explanations of fundamental statistical notions and conceptsCoverage of various types of data and techniques for analysisIn-depth exploration of popular machine learning tools and methodsInsight into specific data science topics, such as social networks and sentiment analysisPractical examples and case studies for real-world applicationRecommended further reading for deeper exploration of specific topics. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Paperback. Condition: New. An accessible introduction to applied data science and machine learning, with minimal math and code required to master the foundational and technical aspects of data science. In Just Enough Data Science and Machine Learning, authors Mark Levene and Martyn Harris present a comprehensive and accessible introduction to data science. It allows the readers to develop an intuition behind the methods adopted in both data science and machine learning, which is the algorithmic component of data science involving the discovery of patterns from input data. This book looks at data science from an applied perspective, where emphasis is placed on the algorithmic aspects of data science and on the fundamental statistical concepts necessary to understand the subject. The book begins by exploring the nature of data science and its origins in basic statistics. The authors then guide readers through the essential steps of data science, starting with exploratory data analysis using visualisation tools. They explain the process of forming hypotheses, building statistical models, and utilising algorithmic methods to discover patterns in the data. Finally, the authors discuss general issues and preliminary concepts that are needed to understand machine learning, which is central to the discipline of data science. The book is packed with practical examples and real-world data sets throughout to reinforce the concepts. All examples are supported by Python code external to the reading material to keep the book timeless. Notable features of this book: Clear explanations of fundamental statistical notions and conceptsCoverage of various types of data and techniques for analysisIn-depth exploration of popular machine learning tools and methodsInsight into specific data science topics, such as social networks and sentiment analysisPractical examples and case studies for real-world applicationRecommended further reading for deeper exploration of specific topics.…

Taschenbuch. Condition: Neu. Neuware -An accessible introduction to applied data science and machine learning, with minimal math and code required to master the foundational and technical aspects of data science. In Just Enough Data Science and Machine Learning, authors Mark Levene and Martyn Harris present a comprehensive and accessible introduction to data science. It allows the readers to develop an intuition behind the methods adopted in both data science and machine learning, which is the algorithmic component of data science involving the discovery of patterns from input data. This book looks at data science from an applied perspective, where emphasis is placed on the algorithmic aspects of data science and on the fundamental statistical concepts necessary to understand the subject. The book begins by exploring the nature of data science and its origins in basic statistics. The authors then guide readers through the essential steps of data science, starting with exploratory data analysis using visualisation tools. They explain the process of forming hypotheses, building statistical models, and utilising algorithmic methods to discover patterns in the data. Finally, the authors discuss general issues and preliminary concepts that are needed to understand machine learning, which is central to the discipline of data science. The book is packed with practical examples and real-world data sets throughout to reinforce the concepts. All examples are supported by Python code external to the reading material to keep the book timeless. Notable features of this book: - Clear explanations of fundamental statistical notions and concepts - Coverage of various types of data and techniques for analysis - In-depth exploration of popular machine learning tools and methods - Insight into specific data science topics, such as social networks and sentiment analysis - Practical examples and case studies for real-world application - Recommended further reading for deeper exploration of specific topics. 224 pp. Englisch.…